Overview

Dataset statistics

Number of variables11
Number of observations5682
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory532.7 KiB
Average record size in memory96.0 B

Variable types

Numeric11

Alerts

gross_revenue is highly overall correlated with qnty_itens and 3 other fieldsHigh correlation
recency_days is highly overall correlated with qnty_invoicesHigh correlation
qnty_itens is highly overall correlated with gross_revenue and 5 other fieldsHigh correlation
qnty_invoices is highly overall correlated with gross_revenue and 5 other fieldsHigh correlation
qnty_products is highly overall correlated with gross_revenue and 4 other fieldsHigh correlation
avg_recency_days is highly overall correlated with qnty_itens and 2 other fieldsHigh correlation
frequency is highly overall correlated with qnty_itens and 2 other fieldsHigh correlation
avg_basket_size is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
avg_unique_basket_size is highly overall correlated with qnty_products and 1 other fieldsHigh correlation
gross_revenue is highly skewed (γ1 = 23.77019547)Skewed
qnty_itens is highly skewed (γ1 = 25.79388576)Skewed
avg_ticket is highly skewed (γ1 = 23.68750171)Skewed
customer_id has unique valuesUnique
avg_recency_days has 2927 (51.5%) zerosZeros

Reproduction

Analysis started2022-12-30 22:18:13.725246
Analysis finished2022-12-30 22:18:24.283084
Duration10.56 seconds
Software versionpandas-profiling vv3.6.1
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct5682
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16605.061
Minimum12347
Maximum22709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size88.8 KiB
2022-12-30T19:18:24.336990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12703.05
Q114292.25
median16232.5
Q318211.75
95-th percentile21742.15
Maximum22709
Range10362
Interquartile range (IQR)3919.5

Descriptive statistics

Standard deviation2808.0265
Coefficient of variation (CV)0.16910667
Kurtosis-0.82234601
Mean16605.061
Median Absolute Deviation (MAD)1959.5
Skewness0.44051451
Sum94349954
Variance7885012.8
MonotonicityNot monotonic
2022-12-30T19:18:24.439163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
21128 1
 
< 0.1%
21110 1
 
< 0.1%
21109 1
 
< 0.1%
21108 1
 
< 0.1%
21107 1
 
< 0.1%
15007 1
 
< 0.1%
17899 1
 
< 0.1%
13833 1
 
< 0.1%
17312 1
 
< 0.1%
Other values (5672) 5672
99.8%
ValueCountFrequency (%)
12347 1
< 0.1%
12348 1
< 0.1%
12349 1
< 0.1%
12350 1
< 0.1%
12352 1
< 0.1%
12353 1
< 0.1%
12354 1
< 0.1%
12355 1
< 0.1%
12356 1
< 0.1%
12357 1
< 0.1%
ValueCountFrequency (%)
22709 1
< 0.1%
22708 1
< 0.1%
22707 1
< 0.1%
22706 1
< 0.1%
22705 1
< 0.1%
22704 1
< 0.1%
22700 1
< 0.1%
22699 1
< 0.1%
22696 1
< 0.1%
22695 1
< 0.1%

gross_revenue
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5446
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1718.7503
Minimum0.42
Maximum278713.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size88.8 KiB
2022-12-30T19:18:24.536024image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile12.851
Q1231.6725
median602.12
Q31548.6825
95-th percentile5203.3905
Maximum278713.22
Range278712.8
Interquartile range (IQR)1317.01

Descriptive statistics

Standard deviation7371.2013
Coefficient of variation (CV)4.2886983
Kurtosis740.70723
Mean1718.7503
Median Absolute Deviation (MAD)470.06
Skewness23.770195
Sum9765939
Variance54334609
MonotonicityNot monotonic
2022-12-30T19:18:24.626651image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.95 9
 
0.2%
7.95 9
 
0.2%
2.95 8
 
0.1%
1.25 8
 
0.1%
12.75 7
 
0.1%
3.75 7
 
0.1%
7.5 7
 
0.1%
1.65 7
 
0.1%
5.95 6
 
0.1%
4.25 6
 
0.1%
Other values (5436) 5608
98.7%
ValueCountFrequency (%)
0.42 1
 
< 0.1%
0.65 1
 
< 0.1%
0.79 1
 
< 0.1%
0.84 4
0.1%
0.85 3
 
0.1%
1.07 1
 
< 0.1%
1.25 8
0.1%
1.44 1
 
< 0.1%
1.65 7
0.1%
1.69 1
 
< 0.1%
ValueCountFrequency (%)
278713.22 1
< 0.1%
259657.3 1
< 0.1%
189861.01 1
< 0.1%
135778.1 1
< 0.1%
123645.86 1
< 0.1%
114691.21 1
< 0.1%
88462.04 1
< 0.1%
65920.12 1
< 0.1%
63031.62 1
< 0.1%
61255.04 1
< 0.1%

recency_days
Real number (ℝ)

Distinct304
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117.30077
Minimum0
Maximum373
Zeros37
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size88.8 KiB
2022-12-30T19:18:24.954242image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q123
median71
Q3200
95-th percentile338
Maximum373
Range373
Interquartile range (IQR)177

Descriptive statistics

Standard deviation111.77505
Coefficient of variation (CV)0.95289272
Kurtosis-0.65445903
Mean117.30077
Median Absolute Deviation (MAD)61.5
Skewness0.80789939
Sum666503
Variance12493.663
MonotonicityNot monotonic
2022-12-30T19:18:25.047083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 110
 
1.9%
4 105
 
1.8%
3 96
 
1.7%
2 90
 
1.6%
10 88
 
1.5%
8 83
 
1.5%
17 79
 
1.4%
9 79
 
1.4%
7 76
 
1.3%
15 66
 
1.2%
Other values (294) 4810
84.7%
ValueCountFrequency (%)
0 37
 
0.7%
1 110
1.9%
2 90
1.6%
3 96
1.7%
4 105
1.8%
5 52
0.9%
7 76
1.3%
8 83
1.5%
9 79
1.4%
10 88
1.5%
ValueCountFrequency (%)
373 24
0.4%
372 22
0.4%
371 17
0.3%
369 4
 
0.1%
368 13
0.2%
367 16
0.3%
366 15
0.3%
365 20
0.4%
364 11
0.2%
362 7
 
0.1%

qnty_itens
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1822
Distinct (%)32.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean933.69095
Minimum1
Maximum196556
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size88.8 KiB
2022-12-30T19:18:25.149521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q1104
median314
Q3794.75
95-th percentile2852.8
Maximum196556
Range196555
Interquartile range (IQR)690.75

Descriptive statistics

Standard deviation4124.5222
Coefficient of variation (CV)4.4174383
Kurtosis992.1513
Mean933.69095
Median Absolute Deviation (MAD)251
Skewness25.793886
Sum5305232
Variance17011683
MonotonicityNot monotonic
2022-12-30T19:18:25.248199image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 115
 
2.0%
2 72
 
1.3%
3 50
 
0.9%
4 49
 
0.9%
5 35
 
0.6%
6 29
 
0.5%
12 24
 
0.4%
72 23
 
0.4%
88 23
 
0.4%
7 21
 
0.4%
Other values (1812) 5241
92.2%
ValueCountFrequency (%)
1 115
2.0%
2 72
1.3%
3 50
0.9%
4 49
0.9%
5 35
 
0.6%
6 29
 
0.5%
7 21
 
0.4%
8 17
 
0.3%
9 7
 
0.1%
10 17
 
0.3%
ValueCountFrequency (%)
196556 1
< 0.1%
77209 1
< 0.1%
76946 1
< 0.1%
69021 1
< 0.1%
64124 1
< 0.1%
63033 1
< 0.1%
61872 1
< 0.1%
57961 1
< 0.1%
57010 1
< 0.1%
49391 1
< 0.1%

qnty_invoices
Real number (ℝ)

Distinct55
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4167547
Minimum1
Maximum202
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size88.8 KiB
2022-12-30T19:18:25.365008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile11
Maximum202
Range201
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.596809
Coefficient of variation (CV)1.9307236
Kurtosis304.25869
Mean3.4167547
Median Absolute Deviation (MAD)0
Skewness13.182002
Sum19414
Variance43.517889
MonotonicityNot monotonic
2022-12-30T19:18:25.467152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2874
50.6%
2 830
 
14.6%
3 502
 
8.8%
4 394
 
6.9%
5 231
 
4.1%
6 178
 
3.1%
7 136
 
2.4%
8 97
 
1.7%
9 67
 
1.2%
11 52
 
0.9%
Other values (45) 321
 
5.6%
ValueCountFrequency (%)
1 2874
50.6%
2 830
 
14.6%
3 502
 
8.8%
4 394
 
6.9%
5 231
 
4.1%
6 178
 
3.1%
7 136
 
2.4%
8 97
 
1.7%
9 67
 
1.2%
10 51
 
0.9%
ValueCountFrequency (%)
202 1
< 0.1%
191 1
< 0.1%
120 1
< 0.1%
90 2
< 0.1%
87 1
< 0.1%
85 1
< 0.1%
71 1
< 0.1%
61 1
< 0.1%
60 1
< 0.1%
55 1
< 0.1%

qnty_products
Real number (ℝ)

Distinct521
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.67969
Minimum1
Maximum6307
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size88.8 KiB
2022-12-30T19:18:25.572163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q114
median40
Q3103
95-th percentile319.95
Maximum6307
Range6306
Interquartile range (IQR)89

Descriptive statistics

Standard deviation191.4163
Coefficient of variation (CV)2.1344442
Kurtosis382.88242
Mean89.67969
Median Absolute Deviation (MAD)32
Skewness15.177168
Sum509560
Variance36640.198
MonotonicityNot monotonic
2022-12-30T19:18:25.666784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 261
 
4.6%
2 150
 
2.6%
3 107
 
1.9%
6 99
 
1.7%
10 95
 
1.7%
4 91
 
1.6%
9 90
 
1.6%
5 88
 
1.5%
7 87
 
1.5%
8 84
 
1.5%
Other values (511) 4530
79.7%
ValueCountFrequency (%)
1 261
4.6%
2 150
2.6%
3 107
1.9%
4 91
 
1.6%
5 88
 
1.5%
6 99
 
1.7%
7 87
 
1.5%
8 84
 
1.5%
9 90
 
1.6%
10 95
 
1.7%
ValueCountFrequency (%)
6307 1
< 0.1%
5095 1
< 0.1%
4563 1
< 0.1%
4295 1
< 0.1%
2441 1
< 0.1%
2051 1
< 0.1%
2036 1
< 0.1%
1723 1
< 0.1%
1662 1
< 0.1%
1634 1
< 0.1%

avg_ticket
Real number (ℝ)

Distinct5490
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.491036
Minimum0.42
Maximum4288.0667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size88.8 KiB
2022-12-30T19:18:25.767899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile3.5083935
Q17.95
median15.765078
Q322.106067
95-th percentile76.32
Maximum4288.0667
Range4287.6467
Interquartile range (IQR)14.156067

Descriptive statistics

Standard deviation116.20947
Coefficient of variation (CV)3.9405014
Kurtosis723.30041
Mean29.491036
Median Absolute Deviation (MAD)7.4804101
Skewness23.687502
Sum167568.07
Variance13504.64
MonotonicityNot monotonic
2022-12-30T19:18:25.860229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.75 11
 
0.2%
4.95 10
 
0.2%
1.25 9
 
0.2%
2.95 9
 
0.2%
7.95 8
 
0.1%
12.75 7
 
0.1%
1.65 7
 
0.1%
8.25 7
 
0.1%
15 6
 
0.1%
4.15 6
 
0.1%
Other values (5480) 5602
98.6%
ValueCountFrequency (%)
0.42 3
0.1%
0.535 1
 
< 0.1%
0.65 1
 
< 0.1%
0.79 1
 
< 0.1%
0.8371428571 1
 
< 0.1%
0.84 2
< 0.1%
0.85 3
0.1%
1.002222222 1
 
< 0.1%
1.02 1
 
< 0.1%
1.045 1
 
< 0.1%
ValueCountFrequency (%)
4288.066667 1
< 0.1%
3861 1
< 0.1%
3202.92 1
< 0.1%
3096 1
< 0.1%
1687.2 1
< 0.1%
1377.077778 1
< 0.1%
1001.2 1
< 0.1%
967.5589091 1
< 0.1%
931.5 1
< 0.1%
910.6735714 1
< 0.1%

avg_recency_days
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1159
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.497378
Minimum0
Maximum366
Zeros2927
Zeros (%)51.5%
Negative0
Negative (%)0.0%
Memory size88.8 KiB
2022-12-30T19:18:25.955576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q358
95-th percentile166
Maximum366
Range366
Interquartile range (IQR)58

Descriptive statistics

Standard deviation61.011957
Coefficient of variation (CV)1.5848341
Kurtosis6.3549791
Mean38.497378
Median Absolute Deviation (MAD)0
Skewness2.3211218
Sum218742.1
Variance3722.4589
MonotonicityNot monotonic
2022-12-30T19:18:26.052623image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2927
51.5%
70 22
 
0.4%
46 17
 
0.3%
55 17
 
0.3%
42 16
 
0.3%
31 16
 
0.3%
28 15
 
0.3%
21 15
 
0.3%
49 15
 
0.3%
56 14
 
0.2%
Other values (1149) 2608
45.9%
ValueCountFrequency (%)
0 2927
51.5%
1 9
 
0.2%
2 4
 
0.1%
2.929133858 1
 
< 0.1%
3 5
 
0.1%
3.351351351 1
 
< 0.1%
3.36036036 1
 
< 0.1%
4 4
 
0.1%
4.191011236 1
 
< 0.1%
4.275862069 1
 
< 0.1%
ValueCountFrequency (%)
366 1
 
< 0.1%
365 1
 
< 0.1%
364 1
 
< 0.1%
363 1
 
< 0.1%
357 2
< 0.1%
356 1
 
< 0.1%
355 2
< 0.1%
352 1
 
< 0.1%
351 2
< 0.1%
350 3
0.1%

frequency
Real number (ℝ)

Distinct1204
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54904729
Minimum0.0054495913
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size88.8 KiB
2022-12-30T19:18:26.156547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0054495913
5-th percentile0.010989011
Q10.024793388
median1
Q31
95-th percentile1
Maximum17
Range16.99455
Interquartile range (IQR)0.97520661

Descriptive statistics

Standard deviation0.55056542
Coefficient of variation (CV)1.002765
Kurtosis139.03774
Mean0.54904729
Median Absolute Deviation (MAD)0
Skewness4.8477436
Sum3119.6867
Variance0.30312229
MonotonicityNot monotonic
2022-12-30T19:18:26.252060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2882
50.7%
2 50
 
0.9%
0.02777777778 18
 
0.3%
0.0625 17
 
0.3%
0.02380952381 16
 
0.3%
0.09090909091 15
 
0.3%
0.02941176471 15
 
0.3%
0.02127659574 13
 
0.2%
0.01923076923 13
 
0.2%
0.07692307692 13
 
0.2%
Other values (1194) 2630
46.3%
ValueCountFrequency (%)
0.005449591281 1
 
< 0.1%
0.005464480874 1
 
< 0.1%
0.005479452055 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005586592179 2
< 0.1%
0.005602240896 1
 
< 0.1%
0.005617977528 2
< 0.1%
0.00566572238 1
 
< 0.1%
0.005681818182 2
< 0.1%
0.005698005698 3
0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
4 1
 
< 0.1%
3 4
 
0.1%
2 50
 
0.9%
1.142857143 1
 
< 0.1%
1 2882
50.7%
0.6666666667 3
 
0.1%
0.5401069519 1
 
< 0.1%
0.5120643432 1
 
< 0.1%
0.5 3
 
0.1%

avg_basket_size
Real number (ℝ)

Distinct2361
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean246.7422
Minimum1
Maximum14149
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size88.8 KiB
2022-12-30T19:18:26.354166image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q173.6875
median150
Q3288
95-th percentile731.95
Maximum14149
Range14148
Interquartile range (IQR)214.3125

Descriptive statistics

Standard deviation436.04325
Coefficient of variation (CV)1.7672017
Kurtosis387.40303
Mean246.7422
Median Absolute Deviation (MAD)96
Skewness14.667628
Sum1401989.2
Variance190133.71
MonotonicityNot monotonic
2022-12-30T19:18:26.443047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 116
 
2.0%
2 71
 
1.2%
3 50
 
0.9%
4 49
 
0.9%
5 35
 
0.6%
6 30
 
0.5%
72 25
 
0.4%
12 25
 
0.4%
88 21
 
0.4%
7 21
 
0.4%
Other values (2351) 5239
92.2%
ValueCountFrequency (%)
1 116
2.0%
2 71
1.2%
3 50
0.9%
3.333333333 1
 
< 0.1%
4 49
0.9%
5 35
 
0.6%
5.666666667 1
 
< 0.1%
6 30
 
0.5%
7 21
 
0.4%
7.5 1
 
< 0.1%
ValueCountFrequency (%)
14149 1
< 0.1%
13956 1
< 0.1%
7824 1
< 0.1%
5963 1
< 0.1%
5197 1
< 0.1%
4300 1
< 0.1%
4280 1
< 0.1%
4278 1
< 0.1%
4136 1
< 0.1%
4049.789474 1
< 0.1%

avg_unique_basket_size
Real number (ℝ)

Distinct1244
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.660301
Minimum1
Maximum1113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size88.8 KiB
2022-12-30T19:18:26.547092image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.3333333
Q18.6666667
median17.666667
Q335
95-th percentile174.95
Maximum1113
Range1112
Interquartile range (IQR)26.333333

Descriptive statistics

Standard deviation77.008201
Coefficient of variation (CV)1.9416948
Kurtosis32.433521
Mean39.660301
Median Absolute Deviation (MAD)11.3125
Skewness5.012874
Sum225349.83
Variance5930.263
MonotonicityNot monotonic
2022-12-30T19:18:26.643957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 283
 
5.0%
2 165
 
2.9%
3 111
 
2.0%
10 107
 
1.9%
6 99
 
1.7%
9 99
 
1.7%
13 96
 
1.7%
7 93
 
1.6%
5 92
 
1.6%
4 91
 
1.6%
Other values (1234) 4446
78.2%
ValueCountFrequency (%)
1 283
5.0%
1.2 1
 
< 0.1%
1.333333333 2
 
< 0.1%
1.5 8
 
0.1%
1.527777778 1
 
< 0.1%
1.666666667 5
 
0.1%
1.8 1
 
< 0.1%
1.833333333 1
 
< 0.1%
1.888888889 1
 
< 0.1%
2 165
2.9%
ValueCountFrequency (%)
1113 1
< 0.1%
748 1
< 0.1%
730 1
< 0.1%
720 1
< 0.1%
704 1
< 0.1%
686 1
< 0.1%
675 1
< 0.1%
674 1
< 0.1%
661 1
< 0.1%
649 1
< 0.1%

Interactions

2022-12-30T19:18:23.138857image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:13.874267image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:14.767990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:15.645150image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:16.554524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:17.493748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:18.426640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:19.537187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:20.392687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:21.299938image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:22.210136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:23.216811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:13.950491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:14.843872image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:15.723516image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:16.636979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:17.575849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:18.503897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:19.611866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:20.471298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:21.379667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:22.291023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:23.294457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:14.034153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:14.918561image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:15.802056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:16.719687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:17.656693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:18.582144image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:19.686983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:20.551800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:21.459163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:22.371257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:23.378036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:14.119982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:15.000614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:15.884442image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:16.808513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:17.743563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:18.664659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:19.767814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:20.636394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:21.541145image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:22.459393image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:23.464988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:14.208693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:15.086301image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:15.974562image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:16.896336image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:17.832462image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:18.751000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:19.853080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:20.726942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:21.629153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:22.548957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:23.550918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:14.293499image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:15.169958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:16.061465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:16.989834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:17.921847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:18.841327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:19.936306image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:20.813295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:21.715693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:22.637838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:23.631139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:14.373736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:15.247910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:16.142863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:17.072590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:18.007153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:18.920596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:20.011036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:20.894622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:21.798972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:22.723263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:23.705704image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:14.446920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:15.320112image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:16.219532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:17.150728image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:18.087276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:18.991608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:20.078703image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:20.971169image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:21.875634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:22.800816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:23.787221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:14.529550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:15.400904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:16.304029image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:17.238231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:18.175345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:19.074227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:20.160687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:21.053993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:21.962529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:22.887948image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:23.870595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:14.609984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:15.483535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:16.389316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:17.326273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:18.261670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:19.157060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:20.240929image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:21.137538image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:22.045084image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:22.973645image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:23.954267image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:14.692593image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:15.568853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:16.474175image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:17.413543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:18.347395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:19.238458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:20.317600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:21.221113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:22.130382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-30T19:18:23.059747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-30T19:18:26.727460image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
customer_idgross_revenuerecency_daysqnty_itensqnty_invoicesqnty_productsavg_ticketavg_recency_daysfrequencyavg_basket_sizeavg_unique_basket_size
customer_id1.000-0.1770.244-0.289-0.383-0.045-0.387-0.3870.381-0.1460.115
gross_revenue-0.1771.000-0.4240.9290.6410.8360.3490.436-0.4520.7280.488
recency_days0.244-0.4241.000-0.495-0.597-0.376-0.150-0.4720.487-0.199-0.036
qnty_itens-0.2890.929-0.4951.0000.6930.7790.3360.501-0.5130.7930.410
qnty_invoices-0.3830.641-0.5970.6931.0000.5300.2700.799-0.8000.161-0.049
qnty_products-0.0450.836-0.3760.7790.5301.000-0.1360.345-0.3590.6130.768
avg_ticket-0.3870.349-0.1500.3360.270-0.1361.0000.230-0.2280.272-0.352
avg_recency_days-0.3870.436-0.4720.5010.7990.3450.2301.000-0.9870.126-0.036
frequency0.381-0.4520.487-0.513-0.800-0.359-0.228-0.9871.000-0.1300.031
avg_basket_size-0.1460.728-0.1990.7930.1610.6130.2720.126-0.1301.0000.606
avg_unique_basket_size0.1150.488-0.0360.410-0.0490.768-0.352-0.0360.0310.6061.000

Missing values

2022-12-30T19:18:24.067705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-30T19:18:24.217664image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgross_revenuerecency_daysqnty_itensqnty_invoicesqnty_productsavg_ticketavg_recency_daysfrequencyavg_basket_sizeavg_unique_basket_size
0178505298.7937216983421025.2323331.00000017.00000049.9411766.176471
1130473089.10561355914920.73221552.8333330.028302150.55555616.555556
2125836629.34249781522729.20414126.5000000.040323331.86666715.133333
313748948.259543952833.86607192.6666670.01792187.8000005.600000
4152914564.60252094149448.55957426.7692310.040115149.5714296.714286
5146885155.03731882026519.45294319.2631580.054496159.40000013.250000
6178095344.66162016115990.58745839.6666670.030726183.2727275.363636
71531161255.0403769390203630.0859724.1910110.240642418.81111122.622222
8160982005.638761376729.93477647.6666670.02439087.5714299.571429
918074489.6037319011337.6615380.0000001.000000190.00000013.000000
customer_idgross_revenuerecency_daysqnty_itensqnty_invoicesqnty_productsavg_ticketavg_recency_daysfrequencyavg_basket_sizeavg_unique_basket_size
567216716336.603114411336.6000000.01.00000044.01.0
56731607394.35297371194.3500000.01.00000037.01.0
567417440177.102472322359.0333330.02.000000116.01.5
567518220298.9222628212149.4600000.01.000000282.02.0
567616998277.503047411277.5000000.01.00000074.01.0
567715098301.951826111301.9500000.01.00000061.01.0
567813217126.421311142263.21000025.00.07692357.01.0
56791310659.50133141159.5000000.01.00000014.01.0
56801774765.45112111165.4500000.01.00000011.01.0
568116462102.002831211102.0000000.01.00000012.01.0